# Imports
import os
import pandas as pd
import csv
import kaggle

# other imports
import numpy as np 
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import ElasticNet
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics import mean_squared_error, mean_absolute_error, classification_report
from sklearn.utils.testing import ignore_warnings
from sklearn.exceptions import ConvergenceWarning
from copy import copy
import seaborn as sns
from scipy.stats import norm
import matplotlib.dates as mdates
# import matplotlib.colors as mcolors
# import random
# import math
# import time
# from sklearn.linear_model import LinearRegression, BayesianRidge
# from sklearn.model_selection import RandomizedSearchCV
from sklearn.tree import DecisionTreeRegressor
# from sklearn.svm import SVR
from datetime import date, datetime
from dateutil.parser import parse
import us
# import operator 
# plt.style.use('fivethirtyeight')
import plotly.graph_objects as go
from plotly.subplots import make_subplots
%matplotlib inline 
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\deprecation.py:143: FutureWarning: The sklearn.utils.testing module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.utils. Anything that cannot be imported from sklearn.utils is now part of the private API.
  warnings.warn(message, FutureWarning)

Covid Tracking Dataset (w/ hospitalised data)

Source: https://covidtracking.com/

Load and Clean the Data

all_cases = pd.read_csv('https://covidtracking.com/api/v1/states/daily.csv')

# Delete unecessary rows
for row in ['negative', 'pending', 'hash', 'negativeIncrease', 'totalTestResults', 'totalTestResultsIncrease', 'dateChecked', 'fips', 'inIcuCumulative', 'onVentilatorCumulative', 'total', 'posNeg', 'deathIncrease', 'hospitalizedIncrease', 'positiveIncrease']:
    del all_cases[row]

# TODO missing values
#      Do we get avg or missing values, or predict them?
#      See https://developerzen.com/data-mining-handling-missing-values-the-database-bd2241882e72

for i, row in all_cases.iterrows():
    # Set Dates
    s = str(row['date'])
    all_cases.at[i, 'date'] = date(year=int(s[0:4]), month=int(s[4:6]), day=int(s[6:8]))

# Missing death figures means no death reports yet
# These are set to 0
for i, row in all_cases.iterrows():
    if np.isnan(row['death']):
        all_cases.at[i, 'death'] = 0

Missing values: Retrieving from other datasets or through merging columns (or both)

The following will be done:

  • Active Cases: Retrieved from JHU dataset and calculating $active = pos-dead-recovered$
  • Beds per State: Retrieved from External Datasets
# TODO Replace active cases with JHU and/or regression model (Selma)
all_cases['active'] = all_cases['positive'] - all_cases['recovered'] - all_cases['death']
# change location of 'active' column
cols = list(all_cases)
cols.insert(3, cols.pop(cols.index('active')))
all_cases = all_cases.loc[:, cols]
# Load datasets for US population and Hospital beds per 1000
us_population = pd.read_csv('data/us_population.csv')
hosp_beds = pd.read_csv('data/hospital_beds.csv')
state_abbrev = pd.read_csv('data/us_state_names.csv')

# add state abbreviations to us_population and hospital beds dataframe
for state in state_abbrev['State'].tolist():
    # store state abbreviation in variable
    abbrev = state_abbrev.loc[state_abbrev['State'] == state, 'Abbreviation'].tolist()[0]
    # add abbrev to new column 'Abbreviation' in us_population df
    us_population.loc[us_population['State'] == state, 'Abbreviation'] = abbrev
    # add abbrev to new column in hosp_beds df
    hosp_beds.loc[hosp_beds['Location'] == state, 'Abbreviation'] = abbrev
    
# change order of columns of us_population
cols = list(us_population)
cols.insert(2, cols.pop(cols.index('Abbreviation')))
us_population = us_population.loc[:, cols]

# drop unnecessary columns of us_population
us_population = us_population.drop(columns=['rank', 'Growth', 'Pop2018', 'Pop2010', 'growthSince2010', 'Percent', 'density'])

# drop unnecessary columns of hosp_beds
hosp_beds = hosp_beds.drop(columns=['Location', 'State/Local Government', 'Non-Profit', 'For-Profit'])

# change order of columns of hosp_beds
cols = list(hosp_beds)
cols.insert(0, cols.pop(cols.index('Abbreviation')))
hosp_beds = hosp_beds.loc[:, cols]
us_population.head()
State Abbreviation Pop
0 Alabama AL 4908621
1 Alaska AK 734002
2 Arizona AZ 7378494
3 Arkansas AR 3038999
4 California CA 39937489
hosp_beds.head()
Abbreviation Total
0 NaN 2.4
1 AL 3.1
2 AK 2.2
3 AZ 1.9
4 AR 3.2
# filter out non-existing states like 'AS'
all_cases = all_cases[all_cases['state'].isin(state_abbrev['Abbreviation'].tolist())]
# see what filtered dataframe looks like
all_cases.head()
date state positive active hospitalizedCurrently hospitalizedCumulative inIcuCurrently onVentilatorCurrently recovered dataQualityGrade ... totalTestsViral positiveTestsViral negativeTestsViral positiveCasesViral commercialScore negativeRegularScore negativeScore positiveScore score grade
0 2020-07-03 AK 1063.0 509.0 25.0 NaN NaN 3.0 539.0 A ... 120208.0 NaN NaN NaN 0 0 0 0 0 NaN
1 2020-07-03 AL 41865.0 18777.0 812.0 2883.0 NaN NaN 22082.0 B ... NaN NaN NaN 41362.0 0 0 0 0 0 NaN
2 2020-07-03 AR 22622.0 6177.0 285.0 1517.0 NaN 70.0 16164.0 A ... NaN NaN NaN 22622.0 0 0 0 0 0 NaN
4 2020-07-03 AZ 91858.0 79592.0 3013.0 5018.0 741.0 489.0 10478.0 A+ ... 577919.0 NaN NaN 91396.0 0 0 0 0 0 NaN
5 2020-07-03 CA 248235.0 NaN 7024.0 NaN 1871.0 NaN NaN B ... 4448176.0 NaN NaN 248235.0 0 0 0 0 0 NaN

5 rows × 25 columns

# Split dataframes by date
df_split_by_date = dict(tuple(all_cases.groupby('date')))

# Split dataframes by state
df_split_by_state = dict(tuple(all_cases.groupby('state')))
# merge dataframes us_population and all_cases
df_merge_uspop = all_cases.merge(us_population, how='left', left_on='state', right_on='Abbreviation')
df_merge_uspop = df_merge_uspop.drop(columns=['Abbreviation'])
df_merge_uspop = df_merge_uspop.rename(columns={'Pop': 'population'})

# change location of 'population' column
cols = list(df_merge_uspop)
cols.insert(2, cols.pop(cols.index('population')))
df_merge_uspop = df_merge_uspop.loc[:, cols]

# merge dataframes hosp_beds and df_merge_uspop
df_merge_hosp = df_merge_uspop.merge(hosp_beds, how='left', left_on='state', right_on='Abbreviation')
df_merge_hosp = df_merge_hosp.drop(columns=['Abbreviation'])
all_cases = df_merge_hosp.rename(columns={'Total': 'bedsPerThousand'})
all_cases.head()
date state population positive active hospitalizedCurrently hospitalizedCumulative inIcuCurrently onVentilatorCurrently recovered ... negativeTestsViral positiveCasesViral commercialScore negativeRegularScore negativeScore positiveScore score grade State bedsPerThousand
0 2020-07-03 AK 734002 1063.0 509.0 25.0 NaN NaN 3.0 539.0 ... NaN NaN 0 0 0 0 0 NaN Alaska 2.2
1 2020-07-03 AL 4908621 41865.0 18777.0 812.0 2883.0 NaN NaN 22082.0 ... NaN 41362.0 0 0 0 0 0 NaN Alabama 3.1
2 2020-07-03 AR 3038999 22622.0 6177.0 285.0 1517.0 NaN 70.0 16164.0 ... NaN 22622.0 0 0 0 0 0 NaN Arkansas 3.2
3 2020-07-03 AZ 7378494 91858.0 79592.0 3013.0 5018.0 741.0 489.0 10478.0 ... NaN 91396.0 0 0 0 0 0 NaN Arizona 1.9
4 2020-07-03 CA 39937489 248235.0 NaN 7024.0 NaN 1871.0 NaN NaN ... NaN 248235.0 0 0 0 0 0 NaN California 1.8

5 rows × 28 columns

# Calculate the total beds, and add the column
all_cases['total_beds'] = all_cases['population'] / 1000 * all_cases['bedsPerThousand']
# change abbreviations to state names
all_cases = all_cases.rename(columns={'state': 'abbrev'})
all_cases = all_cases.rename(columns={'State': 'state'})
# change location of 'state' column
cols = list(all_cases)
cols.insert(1, cols.pop(cols.index('state')))
all_cases = all_cases.loc[:, cols]
all_cases.head()
date state abbrev population positive active hospitalizedCurrently hospitalizedCumulative inIcuCurrently onVentilatorCurrently ... negativeTestsViral positiveCasesViral commercialScore negativeRegularScore negativeScore positiveScore score grade bedsPerThousand total_beds
0 2020-07-03 Alaska AK 734002 1063.0 509.0 25.0 NaN NaN 3.0 ... NaN NaN 0 0 0 0 0 NaN 2.2 1614.8044
1 2020-07-03 Alabama AL 4908621 41865.0 18777.0 812.0 2883.0 NaN NaN ... NaN 41362.0 0 0 0 0 0 NaN 3.1 15216.7251
2 2020-07-03 Arkansas AR 3038999 22622.0 6177.0 285.0 1517.0 NaN 70.0 ... NaN 22622.0 0 0 0 0 0 NaN 3.2 9724.7968
3 2020-07-03 Arizona AZ 7378494 91858.0 79592.0 3013.0 5018.0 741.0 489.0 ... NaN 91396.0 0 0 0 0 0 NaN 1.9 14019.1386
4 2020-07-03 California CA 39937489 248235.0 NaN 7024.0 NaN 1871.0 NaN ... NaN 248235.0 0 0 0 0 0 NaN 1.8 71887.4802

5 rows × 29 columns

  • Load and clean JHU data
  • Merge JHU dataset with main dataset
# This cell takes some time, as it needs to connect to Kaggle Servers to retrieve data
kaggle.api.authenticate()
kaggle.api.dataset_download_files('benhamner/jhucovid19', path='./kaggle/input/jhucovid19/', unzip=True)
# Get Time-Series Data of cases as Pandas DataFrame
dir_jhu = './kaggle/input/jhucovid19/csse_covid_19_data/csse_covid_19_daily_reports'

df_list = []
for dirname, _, files in os.walk(dir_jhu):
    for file in files:
        if 'gitignore' not in file and 'README' not in file:
            full_dir = os.path.join(dirname, file)
            df_list.append(pd.read_csv(full_dir))
            
jhu_df = pd.concat(df_list, axis=0, ignore_index=True, sort=True)

# convert Last Update columns to datetime format
jhu_df.loc[:, 'Last Update'] = pd.to_datetime(jhu_df['Last Update']).apply(lambda x: x.date())
jhu_df.loc[:, 'Last_Update'] = pd.to_datetime(jhu_df['Last_Update']).apply(lambda x: x.date())

# Combine Last Update with Last_Update
jhu_df['LastUpdate'] = jhu_df['Last_Update'].combine_first(jhu_df['Last Update'])

# Combine Country/Region with Country_Region
jhu_df['CountryRegion'] = jhu_df['Country/Region'].combine_first(jhu_df['Country_Region'])

# Retrieve only US data
jhu_df = jhu_df[jhu_df['CountryRegion']=='US']

# Combine Province/State with Province_State
jhu_df['ProvinceState'] = jhu_df['Province/State'].combine_first(jhu_df['Province_State'])

# Drop unnecessary columns
jhu_df = jhu_df.drop(['Admin2', 'Lat', 'Latitude', 'Long_', 'Longitude', 'Combined_Key', 'Country/Region',
                      'Country_Region', 'Province/State', 'Province_State',
                      'Last Update', 'Last_Update', 'FIPS'], axis=1)

# Change column order
cols = list(jhu_df)
cols.insert(0, cols.pop(cols.index('CountryRegion')))
cols.insert(1, cols.pop(cols.index('ProvinceState')))
cols.insert(2, cols.pop(cols.index('LastUpdate')))
jhu_df = jhu_df.loc[:, cols]

# Change region to known US states
state_abbrs_dict = {}
for state in us.states.STATES:
    state_abbrs_dict[state.abbr] = state.name

def toState(input_state, mapping):
    abbreviation = input_state.rstrip()[-2:]
    try:
        return_value = mapping[abbreviation]
    except KeyError:
        return_value = input_state
    return return_value

jhu_df['ProvinceState'] = jhu_df['ProvinceState'].apply(lambda x: toState(x, state_abbrs_dict) if x != 'Washington, D.C.' else 'District of Columbia')

# Filter out unknown states
jhu_df = jhu_df[jhu_df['ProvinceState'].isin(all_cases.state.unique().tolist())]

# Merge-sum rows with same date and State
jhu_df = jhu_df.groupby(['LastUpdate', 'ProvinceState']).agg(
    {
        'Active': sum,
        'Confirmed': sum,
        'Deaths': sum,
        'Recovered': sum
    }
).reset_index()

jhu_df.tail()
LastUpdate ProvinceState Active Confirmed Deaths Recovered
5802 2020-07-01 Virginia 61024.0 62787.0 1763.0 0.0
5803 2020-07-01 Washington 31492.0 32824.0 1332.0 0.0
5804 2020-07-01 West Virginia 2812.0 2905.0 93.0 0.0
5805 2020-07-01 Wisconsin 27875.0 28659.0 784.0 0.0
5806 2020-07-01 Wyoming 1467.0 1487.0 20.0 0.0
# Now that we have the JHU dataset relatively cleaned
# we can go ahead and merge its data with our main dataset

for i, row in all_cases.iterrows():
    last_update = all_cases.at[i, 'date']
    state = all_cases.at[i, 'state']
    matching_row = jhu_df[jhu_df['ProvinceState'] == state]
    matching_row = matching_row[matching_row['LastUpdate'] == last_update].reset_index()

    if len(matching_row.values) > 0:
        #all_cases.at[i, 'positive'] = matching_row['Confirmed'].values[0]
        all_cases.at[i, 'active'] = matching_row['Active'].values[0]
        #all_cases.at[i, 'recovered'] = matching_row['Recovered'].values[0]   --- JHU was inconsistent, therefore removed
        #all_cases.at[i, 'death'] = matching_row['Deaths'].values[0]

    # Replace unknown recovery numbers with 0
    if np.isnan(row['recovered']):
        all_cases.at[i, 'recovered'] = 0

    if all_cases.at[i, 'active'] == 0 or np.isnan(row['active']):
        positive = all_cases.at[i, 'positive']
        recovered = all_cases.at[i, 'recovered']
        dead = all_cases.at[i, 'death']
        all_cases.at[i, 'active'] = positive - recovered - dead

all_cases.tail()
date state abbrev population positive active hospitalizedCurrently hospitalizedCumulative inIcuCurrently onVentilatorCurrently ... negativeTestsViral positiveCasesViral commercialScore negativeRegularScore negativeScore positiveScore score grade bedsPerThousand total_beds
6182 2020-01-26 Washington WA 7797095 2.0 2.0 NaN NaN NaN NaN ... NaN NaN 0 0 0 0 0 NaN 1.7 13255.0615
6183 2020-01-25 Washington WA 7797095 2.0 2.0 NaN NaN NaN NaN ... NaN NaN 0 0 0 0 0 NaN 1.7 13255.0615
6184 2020-01-24 Washington WA 7797095 2.0 2.0 NaN NaN NaN NaN ... NaN NaN 0 0 0 0 0 NaN 1.7 13255.0615
6185 2020-01-23 Washington WA 7797095 2.0 2.0 NaN NaN NaN NaN ... NaN NaN 0 0 0 0 0 NaN 1.7 13255.0615
6186 2020-01-22 Washington WA 7797095 2.0 2.0 NaN NaN NaN NaN ... NaN NaN 0 0 0 0 0 NaN 1.7 13255.0615

5 rows × 29 columns

# Save formatted dataset offline in case of disaster
dataset_file = 'results/all_cases.csv'
all_cases.to_csv(dataset_file)
# convert date to datetime format
all_cases['date'] = pd.to_datetime(all_cases['date'])

An Exploratory data analysis of the US dataset

Basic triad of the dataset: validating data types and data integrity of each row

dataset_file = 'results/all_cases.csv'
covid_df = pd.read_csv(dataset_file, index_col=0) 
# convert date to datetime format
covid_df['date'] = pd.to_datetime(covid_df['date'])
covid_df.info()
# set float format to 3 decimals
pd.set_option('display.float_format', lambda x: '%.3f' % x)
<class 'pandas.core.frame.DataFrame'>
Int64Index: 6187 entries, 0 to 6186
Data columns (total 29 columns):
date                      6187 non-null datetime64[ns]
state                     6187 non-null object
abbrev                    6187 non-null object
population                6187 non-null int64
positive                  6187 non-null float64
active                    6187 non-null float64
hospitalizedCurrently     4057 non-null float64
hospitalizedCumulative    3414 non-null float64
inIcuCurrently            2052 non-null float64
onVentilatorCurrently     1789 non-null float64
recovered                 6187 non-null float64
dataQualityGrade          5253 non-null object
lastUpdateEt              5832 non-null object
dateModified              5832 non-null object
checkTimeEt               5832 non-null object
death                     6187 non-null float64
hospitalized              3414 non-null float64
totalTestsViral           1712 non-null float64
positiveTestsViral        585 non-null float64
negativeTestsViral        590 non-null float64
positiveCasesViral        3347 non-null float64
commercialScore           6187 non-null int64
negativeRegularScore      6187 non-null int64
negativeScore             6187 non-null int64
positiveScore             6187 non-null int64
score                     6187 non-null int64
grade                     0 non-null float64
bedsPerThousand           6187 non-null float64
total_beds                6187 non-null float64
dtypes: datetime64[ns](1), float64(16), int64(6), object(6)
memory usage: 1.4+ MB
covid_df.head()
date state abbrev population positive active hospitalizedCurrently hospitalizedCumulative inIcuCurrently onVentilatorCurrently ... negativeTestsViral positiveCasesViral commercialScore negativeRegularScore negativeScore positiveScore score grade bedsPerThousand total_beds
0 2020-07-03 Alaska AK 734002 1063.000 509.000 25.000 nan nan 3.000 ... nan nan 0 0 0 0 0 nan 2.200 1614.804
1 2020-07-03 Alabama AL 4908621 41865.000 18777.000 812.000 2883.000 nan nan ... nan 41362.000 0 0 0 0 0 nan 3.100 15216.725
2 2020-07-03 Arkansas AR 3038999 22622.000 6177.000 285.000 1517.000 nan 70.000 ... nan 22622.000 0 0 0 0 0 nan 3.200 9724.797
3 2020-07-03 Arizona AZ 7378494 91858.000 79592.000 3013.000 5018.000 741.000 489.000 ... nan 91396.000 0 0 0 0 0 nan 1.900 14019.139
4 2020-07-03 California CA 39937489 248235.000 241972.000 7024.000 nan 1871.000 nan ... nan 248235.000 0 0 0 0 0 nan 1.800 71887.480

5 rows × 29 columns

The NaN values may indicate that there were no to few Covid-19 patients at these date points. We further analyse the statistical values of the dataset columns to ensure data integrity and accuracy.

covid_df.describe()
# TODO rounding up the numbers
population positive active hospitalizedCurrently hospitalizedCumulative inIcuCurrently onVentilatorCurrently recovered death hospitalized ... negativeTestsViral positiveCasesViral commercialScore negativeRegularScore negativeScore positiveScore score grade bedsPerThousand total_beds
count 6187.000 6187.000 6187.000 4057.000 3414.000 2052.000 1789.000 6187.000 6187.000 3414.000 ... 590.000 3347.000 6187.000 6187.000 6187.000 6187.000 6187.000 0.000 6187.000 6187.000
mean 6541047.150 22448.958 20684.635 976.125 4496.786 434.262 216.229 4891.973 1152.022 4496.786 ... 319529.515 33575.520 0.000 0.000 0.000 0.000 0.000 nan 2.626 15803.983
std 7386652.501 48404.581 44568.693 1864.136 13066.426 700.329 320.857 11736.161 2995.990 13066.426 ... 420218.699 57939.209 0.000 0.000 0.000 0.000 0.000 nan 0.744 16159.025
min 567025.000 0.000 0.000 1.000 0.000 1.000 0.000 0.000 0.000 0.000 ... 17.000 0.000 0.000 0.000 0.000 0.000 0.000 nan 1.600 1318.928
25% 1778070.000 698.000 637.000 104.000 238.000 77.000 35.000 0.000 15.000 238.000 ... 52213.750 5235.500 0.000 0.000 0.000 0.000 0.000 nan 2.100 3773.952
50% 4499692.000 5544.000 5214.000 394.000 1032.000 168.000 91.000 323.000 163.000 1032.000 ... 154207.000 14539.000 0.000 0.000 0.000 0.000 0.000 nan 2.500 11557.920
75% 7797095.000 22753.000 20815.500 938.000 3445.750 453.250 238.000 3413.000 840.000 3445.750 ... 399926.000 37408.000 0.000 0.000 0.000 0.000 0.000 nan 3.100 19124.737
max 39937489.000 395872.000 553611.000 18825.000 89995.000 5225.000 2425.000 93572.000 24885.000 89995.000 ... 2279841.000 395872.000 0.000 0.000 0.000 0.000 0.000 nan 4.800 71887.480

8 rows × 22 columns

# drop unnecessary columns
covid_cleaned = covid_df.drop(['hospitalized', 'bedsPerThousand'], axis=1)
covid_100k = covid_cleaned.copy()
# list of columns to transform to per 100k
columns_list = ['positive', 'active', 'recovered', 'death', 'hospitalizedCurrently', 'hospitalizedCumulative', 'inIcuCurrently', 'onVentilatorCurrently', 'total_beds']
# add columns per 100k
for column in columns_list:
    if column == 'total_beds':
        covid_100k['BedsPer100k'.format(column)] = (covid_cleaned.loc[:, column] / covid_cleaned.loc[:, 'population']) * 100000
    else:
        covid_100k['{}_100k'.format(column)] = (covid_cleaned.loc[:, column] / covid_cleaned.loc[:, 'population']) * 100000

covid_100k = covid_100k.drop(columns_list, axis=1)
covid_100k['date'] = pd.to_datetime(covid_100k['date'])
start_date = '2020-04-18'
end_date = '2020-05-19'
mask = (covid_100k['date'] > start_date) & (covid_100k['date'] <= end_date)
covid_100k_last_month = covid_100k.loc[mask]
covid_100k_last_month_part1 =  covid_100k_last_month.groupby('date').sum().loc[:, ['positive_100k','active_100k','recovered_100k','death_100k','hospitalizedCumulative_100k']].diff(periods=1, axis=0)

covid_100k_last_month_part2 = covid_100k_last_month.groupby('date').sum().loc[:, ['inIcuCurrently_100k','onVentilatorCurrently_100k','BedsPer100k']]

final_100k_last_month = covid_100k_last_month_part1.merge(covid_100k_last_month_part2, left_index=True, right_index=True)
final_100k_last_month.head()
positive_100k active_100k recovered_100k death_100k hospitalizedCumulative_100k inIcuCurrently_100k onVentilatorCurrently_100k BedsPer100k
date
2020-04-19 nan nan nan nan nan 152.818 80.717 13440.000
2020-04-20 413.759 391.692 35.481 25.728 22.652 155.542 79.710 13440.000
2020-04-21 387.394 360.446 65.218 30.520 31.446 164.605 78.603 13440.000
2020-04-22 428.601 989.954 412.625 28.780 36.181 165.884 78.032 13440.000
2020-04-23 452.031 -2213.482 72.921 26.282 28.842 164.122 94.521 13440.000
final_100k_last_month.describe()
positive_100k active_100k recovered_100k death_100k hospitalizedCumulative_100k inIcuCurrently_100k onVentilatorCurrently_100k BedsPer100k
count 30.000 30.000 30.000 30.000 30.000 31.000 31.000 31.000
mean 399.188 364.943 147.172 23.063 39.160 139.595 73.503 13440.000
std 58.939 634.169 81.341 6.102 43.524 17.123 8.141 0.000
min 287.019 -2213.482 35.481 13.053 9.507 109.602 61.622 13440.000
25% 348.980 314.204 80.563 17.951 22.991 126.370 66.261 13440.000
50% 405.026 366.234 127.774 24.119 28.295 140.327 74.706 13440.000
75% 432.647 419.664 212.491 26.243 32.754 151.795 79.157 13440.000
max 544.349 2291.210 412.625 33.917 246.371 165.884 94.521 13440.000
# save description cleaned dataset to csv
describe_file = 'results/final_100k_last_month.csv'
final_100k_last_month.describe().to_csv(describe_file)

Graphical Exploratory Analysis

Plotting histograms, scatterplots and boxplots to assess the distribution of the entire US dataset.

# Omitting the categorical (states/abbreviations) and time columns 
# There must be an easier way for you, but this was the easiest way I could think of
covid_cleaned['date'] = pd.to_datetime(covid_cleaned['date'])
# mask data for last month
start_date = '2020-04-18'
end_date = '2020-05-19'
mask = (covid_cleaned['date'] > start_date) & (covid_cleaned['date'] <= end_date)
covid_cleaned_last_month = covid_cleaned.loc[mask]
plot_df = covid_cleaned_last_month[['population', 'active', 'recovered', 'death', 'hospitalizedCurrently', 'inIcuCurrently', 'onVentilatorCurrently', 'total_beds']]
plot_df_last_month = covid_100k_last_month[['population', 'active_100k', 'recovered_100k', 'death_100k', 'hospitalizedCurrently_100k', 'inIcuCurrently_100k', 'onVentilatorCurrently_100k', 'BedsPer100k']]
timeseries_usa_df = covid_100k.loc[:, ['date', 'positive_100k', 'active_100k', 'recovered_100k', 'death_100k', 'hospitalizedCurrently_100k', 'inIcuCurrently_100k', 'onVentilatorCurrently_100k', 'BedsPer100k']].groupby('date').sum().reset_index()
# timeseries_usa_df['log_positive'] = np.log(timeseries_usa_df['positive_100k'])
# timeseries_usa_df['log_active'] = np.log(timeseries_usa_df['active_100k'])
# timeseries_usa_df['log_recovered'] = np.log(timeseries_usa_df['recovered_100k'])
# timeseries_usa_df['log_death'] = np.log(timeseries_usa_df['death_100k'])
timeseries_usa_df.tail()
date positive_100k active_100k recovered_100k death_100k hospitalizedCurrently_100k inIcuCurrently_100k onVentilatorCurrently_100k BedsPer100k
159 2020-06-29 35834.140 34810.986 13269.151 1605.037 416.665 67.216 32.901 13440.000
160 2020-06-30 36369.224 33327.208 13616.885 1612.910 430.910 66.869 33.482 13440.000
161 2020-07-01 36972.209 34868.331 13814.722 1622.259 443.622 67.228 34.721 13440.000
162 2020-07-02 37608.249 21593.275 14384.117 1630.857 452.273 69.727 35.282 13440.000
163 2020-07-03 38289.967 22177.125 14473.039 1639.803 451.256 71.286 34.451 13440.000
# get data from last day
# plot_df_last_date = plot_df.loc[covid_df['date'] == '2020-05-18'] 

# Plotting histograms to gain insight of the distribution shape, skewness and scale
fig, axs = plt.subplots(4,2,figsize = (16, 16))
sns.set()
for i, column in enumerate(plot_df_last_month.columns):
    if (i + 1) % 2 == 0:
        ax = axs[(i//2), 1]
    else:
        ax = axs[(i//2), 0]
    sns.distplot(plot_df_last_month[column], fit=norm, fit_kws=dict(label='normality'), hist_kws=dict(color='plum', edgecolor='k', linewidth=1, label='frequency'), ax=ax, color='#9d53ad')
    ax.legend(loc='upper right')
plt.tight_layout()
fig.subplots_adjust(top=0.95)
# Looking at linearity and variance with scatterplots
# Removing the target variable and saving it in another df
target = plot_df.hospitalizedCurrently
indep_var = plot_df.drop(columns=['hospitalizedCurrently'])

fig, ax = plt.subplots(figsize = (16, 16))
for i, col in enumerate(indep_var.columns):
    ax=fig.add_subplot(4, 3, i+1) 
    sns.regplot(x=indep_var[col], y=target, data=indep_var, label=col, scatter_kws={'s':10}, line_kws={"color": "plum", 'label': 'hospitCurr'})
    plt.suptitle('Scatterplots with Target Hospitalized Patients Showing Growth Trajectories', fontsize=23)
    plt.legend()
plt.tight_layout()
fig.subplots_adjust(top=0.95)
# Assessing the normality of the distribution with a boxplot
# Boxplot with removed outliers
fig, ax = plt.subplots(figsize = (16, 12))
for i, col in enumerate(plot_df.columns):
    ax=fig.add_subplot(4, 3, i+1) 
    sns.boxplot(x=plot_df[col], data=plot_df, color='lightblue', showfliers=False)
    plt.suptitle('Boxplots of Independent Variables', fontsize=23)
plt.tight_layout()
fig.subplots_adjust(top=0.95)
# get data from last day
plot_df_last_date = plot_df.loc[covid_df['date'] == '2020-05-18'] 

fig, ax = plt.subplots(figsize = (16, 12))
for i, col in enumerate(plot_df_last_date.columns):
    ax=fig.add_subplot(4, 3, i+1) 
    sns.boxplot(x=plot_df_last_date[col], data=plot_df, color='lightblue', showfliers=True)
    plt.suptitle('Boxplots of Independent Variables', fontsize=23)
plt.tight_layout()
fig.subplots_adjust(top=0.95)

Analysis of Hospitalizations by State

New York:

# Split covid_df into subset with only NY values
new_york = covid_df.loc[covid_df['abbrev'] == 'NY'] 
fig, ax = plt.subplots(figsize = (16, 12))
# Timeseries plt
plt.plot(new_york.date, new_york.hospitalizedCurrently, linewidth=3.3)
plt.title('Number of Patients in NY Currently Hospitalized', fontsize=23)
plt.xlabel('Date')
plt.ylabel('No. Patients')
C:\Users\Doctor Gomez\AppData\Roaming\Python\Python37\site-packages\pandas\plotting\_converter.py:129: FutureWarning:

Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.

To register the converters:
	>>> from pandas.plotting import register_matplotlib_converters
	>>> register_matplotlib_converters()

Text(0, 0.5, 'No. Patients')
# Omit the categorical and date cols 
new_york = new_york[['positive', 'active', 'hospitalizedCurrently', 'hospitalizedCumulative', 'inIcuCurrently', 'recovered', 'death', 'hospitalized']]
# Scatter plots NY
# Split dependent var from independent variables
target_ny = new_york.hospitalizedCurrently
indep_var_ny = new_york.drop(columns=['hospitalizedCurrently'])

fig, ax = plt.subplots(figsize = (16, 16))
for i, col in enumerate(indep_var_ny.columns):
    ax=fig.add_subplot(3, 3, i+1) 
    sns.regplot(x=indep_var_ny[col], y=target_ny, data=indep_var_ny, label=col, scatter_kws={'s':10}, line_kws={"color": "plum"})
    plt.suptitle('Distributions of Independent Variables NY', fontsize=23)
plt.tight_layout()
fig.subplots_adjust(top=0.95)
# Boxplot of NY
fig, ax = plt.subplots(figsize = (16, 16))
for i, col in enumerate(new_york.columns):
    ax=fig.add_subplot(3, 3, i+1) 
    sns.boxplot(x=new_york[col], data=new_york, color='lightpink', showfliers=True)
    plt.suptitle('Boxplots of Independent Variables NY', fontsize=23)
plt.tight_layout()
fig.subplots_adjust(top=0.95)

California:

cali = covid_df.loc[(covid_df['abbrev'] == 'CA') & (covid_df['state']== 'California')] 
# TODO fix legend/axis/plot alltogether
# Timeseries plt
fig, ax = plt.subplots(figsize = (16, 12))
plt.plot(cali.date, cali.hospitalizedCurrently, linewidth=4.7)
plt.title('Number of Patients in CA Currently Hospitalized', fontsize=23)
plt.xlabel('Date')
plt.ylabel('No. Patients')
Text(0, 0.5, 'No. Patients')
# Checking which cols have NaN values
cali[['positive', 'active', 'hospitalizedCurrently', 'inIcuCurrently', 'recovered', 'death', 'hospitalized']]
cali.head()

# Omit the NaN cols
cali = cali[['positive', 'active', 'hospitalizedCurrently', 'inIcuCurrently', 'recovered', 'death']]
# Scatter plots CA
# Split dependent var from independent variables
target_ca = cali.hospitalizedCurrently
indep_var_ca = cali.drop(columns=['hospitalizedCurrently'])

fig, ax = plt.subplots(figsize = (16, 16))
for i, col in enumerate(indep_var_ca.columns):
    ax=fig.add_subplot(2, 3, i+1) 
    sns.regplot(x=indep_var_ca[col], y=target_ca, data=indep_var_ca, label=col, scatter_kws={'s':10}, line_kws={"color": "plum"})
    plt.suptitle('Distributions of Independent Variables CA', fontsize=23)
plt.tight_layout()
fig.subplots_adjust(top=0.95)
# Boxplot of CA
fig, ax = plt.subplots(figsize = (16, 16))
for i, col in enumerate(cali.columns):
    ax=fig.add_subplot(3, 3, i+1) 
    sns.boxplot(x=cali[col], data=cali, color='lightpink', showfliers=True)
    plt.suptitle('Boxplots of Independent Variables CA', fontsize=23)
plt.tight_layout()
fig.subplots_adjust(top=0.95)
###endcali

Texas:

texas = covid_df.loc[(covid_df['abbrev'] == 'TX') & (covid_df['state']== 'Texas')] 
# TODO fix legend/axis/plot alltogether
# Timeseries plt
fig, ax = plt.subplots(figsize = (16, 12))
plt.plot(texas.date, texas.hospitalizedCurrently, linewidth=4.7, color='r')
plt.title('Number of Patients in TX Currently Hospitalized', fontsize=23)
plt.xlabel('Date')
plt.ylabel('No. Patients')
Text(0, 0.5, 'No. Patients')
# TODO fix legend/axis/plot alltogether
# Timeseries plt
fig, ax = plt.subplots(figsize = (16, 12))
plt.plot(texas.date, texas.death, linewidth=4.7, color='r')
plt.title('Number of Cummulative Deaths in Texas', fontsize=23)
plt.xlabel('Date')
plt.ylabel('No. Patients')
Text(0, 0.5, 'No. Patients')
# TODO fix legend/axis/plot alltogether
# Timeseries plt
fig, ax = plt.subplots(figsize = (16, 12))
plt.plot(texas.date, texas.totalTestsViral, linewidth=4.7, color='r')
plt.title('Number of Cummulative Viral Tests in Texas', fontsize=23)
plt.xlabel('Date')
plt.ylabel('No. Patients')
Text(0, 0.5, 'No. Patients')
# Checking which cols have NaN values
texas[['positive', 'active', 'hospitalizedCurrently', 'inIcuCurrently', 'recovered', 'death', 'hospitalized']]
texas.head()

# Omit the NaN cols
texas = texas[['positive', 'active', 'hospitalizedCurrently', 'inIcuCurrently', 'recovered', 'death']]
# Scatter plots TX
# Split dependent var from independent variables
target_tx = texas.hospitalizedCurrently
indep_var_tx = texas.drop(columns=['hospitalizedCurrently'])

fig, ax = plt.subplots(figsize = (16, 16))
for i, col in enumerate(indep_var_tx.columns):
    ax=fig.add_subplot(2, 3, i+1) 
    sns.regplot(x=indep_var_tx[col], y=target_tx, data=indep_var_tx, label=col, scatter_kws={'s':10}, line_kws={"color": "plum"})
    plt.suptitle('Distributions of Independent Variables TX', fontsize=23)
plt.tight_layout()
fig.subplots_adjust(top=0.95)
# Boxplot of TX
fig, ax = plt.subplots(figsize = (16, 16))
for i, col in enumerate(texas.columns):
    ax=fig.add_subplot(3, 3, i+1) 
    sns.boxplot(x=texas[col], data=texas, color='lightpink', showfliers=True)
    plt.suptitle('Boxplots of Independent Variables TX', fontsize=18)
plt.tight_layout()
fig.subplots_adjust(top=0.95)
###endtx

South Carolina:

sc = covid_df.loc[(covid_df['abbrev'] == 'SC') & (covid_df['state']== 'South Carolina')] 
# TODO fix legend/axis/plot alltogether
# Timeseries plt
fig, ax = plt.subplots(figsize = (16, 12))
plt.plot(sc.date, sc.hospitalizedCurrently, linewidth=4.7, color='r')
plt.title('Number of Patients in South Carolina Currently Hospitalized', fontsize=23)
plt.xlabel('Date')
plt.ylabel('No. Patients')
Text(0, 0.5, 'No. Patients')
# TODO fix legend/axis/plot alltogether
# Timeseries plt
fig, ax = plt.subplots(figsize = (16, 12))
plt.plot(sc.date, sc.death, linewidth=4.7, color='r')
plt.title('Number of Cummulative Deaths in South Carolina', fontsize=23)
plt.xlabel('Date')
plt.ylabel('No. Patients')
Text(0, 0.5, 'No. Patients')
# TODO fix legend/axis/plot alltogether
# Timeseries plt
fig, ax = plt.subplots(figsize = (16, 12))
plt.plot(sc.date, sc.totalTestsViral, linewidth=4.7, color='r')
plt.title('Number of Cummulative Viral Tests in South Carolina', fontsize=23)
plt.xlabel('Date')
plt.ylabel('No. Patients')
Text(0, 0.5, 'No. Patients')
# TODO fix legend/axis/plot alltogether
# Timeseries plt
fig, ax = plt.subplots(figsize = (16, 12))
plt.plot(sc.date, sc.positiveTestsViral, linewidth=4.7, color='r')
plt.title('Number of Cummulative Positive Viral Tests in South Carolina', fontsize=23)
plt.xlabel('Date')
plt.ylabel('No. Patients')
Text(0, 0.5, 'No. Patients')
# TODO fix legend/axis/plot alltogether
# Timeseries plt
fig, ax = plt.subplots(figsize = (16, 12))
plt.plot(sc.date, sc.positiveTestsViral/sc.totalTestsViral*100, linewidth=4.7, color='r')
plt.title('Viral Infection Rate in South Carolina', fontsize=23)
plt.xlabel('Date')
plt.ylabel('% Infection Rate')
Text(0, 0.5, '% Infection Rate')
# Checking which cols have NaN values
sc[['positive', 'active', 'hospitalizedCurrently', 'inIcuCurrently', 'recovered', 'death', 'hospitalized']]
sc.head()

# Omit the NaN cols
sc = sc[['positive', 'active', 'hospitalizedCurrently', 'inIcuCurrently', 'recovered', 'death']]
# Scatter plots SC
# Split dependent var from independent variables
target_sc = sc.hospitalizedCurrently
indep_var_sc = sc.drop(columns=['hospitalizedCurrently'])

fig, ax = plt.subplots(figsize = (16, 16))
for i, col in enumerate(indep_var_sc.columns):
    ax=fig.add_subplot(2, 3, i+1) 
    sns.regplot(x=indep_var_sc[col], y=target_sc, data=indep_var_sc, label=col, scatter_kws={'s':10}, line_kws={"color": "plum"})
    plt.suptitle('Distributions of Independent Variables South Carolina', fontsize=23)
plt.tight_layout()
fig.subplots_adjust(top=0.95)
# Boxplot of SC
fig, ax = plt.subplots(figsize = (16, 16))
for i, col in enumerate(sc.columns):
    ax=fig.add_subplot(3, 3, i+1) 
    sns.boxplot(x=sc[col], data=sc, color='lightpink', showfliers=True)
    plt.suptitle('Boxplots of Independent Variables South Carolina', fontsize=18)
plt.tight_layout()
fig.subplots_adjust(top=0.95)
###endsouthcarolina

Nevada:

nevada = covid_df.loc[(covid_df['abbrev'] == 'NV') & (covid_df['state']== 'Nevada')] 
# TODO fix legend/axis/plot alltogether
# Timeseries plt
fig, ax = plt.subplots(figsize = (16, 12))
plt.plot(nevada.date, nevada.hospitalizedCurrently, linewidth=4.7)
plt.title('Number of Patients in Nevada Currently Hospitalized', fontsize=23)
plt.xlabel('Date')
plt.ylabel('No. Patients')
Text(0, 0.5, 'No. Patients')
# TODO fix legend/axis/plot alltogether
# Timeseries plt
fig, ax = plt.subplots(figsize = (16, 12))
plt.plot(nevada.date, nevada.inIcuCurrently, linewidth=4.7)
plt.title('Number of Patients in Nevada Currently Hospitalized in ICU', fontsize=23)
plt.xlabel('Date')
plt.ylabel('No. Patients')
Text(0, 0.5, 'No. Patients')
# TODO fix legend/axis/plot alltogether
# Timeseries plt
fig, ax = plt.subplots(figsize = (16, 12))
plt.plot(nevada.date, nevada.onVentilatorCurrently, linewidth=4.7)
plt.title('Number of Patients in Nevada Currently on a Ventilator', fontsize=23)
plt.xlabel('Date')
plt.ylabel('No. Patients')
Text(0, 0.5, 'No. Patients')
# Checking which cols have NaN values
nevada[['positive', 'active', 'hospitalizedCurrently', 'inIcuCurrently', 'recovered', 'death', 'hospitalized']]
nevada.head()

# Omit the NaN cols
nevada = nevada[['positive', 'active', 'hospitalizedCurrently', 'inIcuCurrently', 'recovered', 'death']]
# Scatter plots NV
# Split dependent var from independent variables
target_nv = nevada.hospitalizedCurrently
indep_var_nv = nevada.drop(columns=['hospitalizedCurrently'])

fig, ax = plt.subplots(figsize = (16, 16))
for i, col in enumerate(indep_var_nv.columns):
    ax=fig.add_subplot(2, 3, i+1) 
    sns.regplot(x=indep_var_nv[col], y=target_nv, data=indep_var_nv, label=col, scatter_kws={'s':10}, line_kws={"color": "plum"})
    plt.suptitle('Distributions of Independent Variables Nevada', fontsize=23)
plt.tight_layout()
fig.subplots_adjust(top=0.95)
# Boxplot of NV
fig, ax = plt.subplots(figsize = (16, 16))
for i, col in enumerate(nevada.columns):
    ax=fig.add_subplot(3, 3, i+1) 
    sns.boxplot(x=nevada[col], data=nevada, color='lightpink', showfliers=True)
    plt.suptitle('Boxplots of Independent Variables Nevada', fontsize=23)
plt.tight_layout()
fig.subplots_adjust(top=0.95)
###endnevada

Arizona:

arizona = covid_df.loc[(covid_df['abbrev'] == 'AZ') & (covid_df['state']== 'Arizona')] 
# TODO fix legend/axis/plot alltogether
# Timeseries plt
fig, ax = plt.subplots(figsize = (16, 12))
plt.plot(arizona.date, arizona.hospitalizedCurrently, linewidth=4.7, color='r')
plt.title('Number of Patients in Arizona Currently Hospitalized', fontsize=23)
plt.xlabel('Date')
plt.ylabel('No. Patients')
Text(0, 0.5, 'No. Patients')
# TODO fix legend/axis/plot alltogether
# Timeseries plt
fig, ax = plt.subplots(figsize = (16, 12))
plt.plot(arizona.date, arizona.inIcuCurrently, linewidth=4.7, color='r')
plt.title('Number of Patients in Arizona Currently Hospitalized in ICU', fontsize=23)
plt.xlabel('Date')
plt.ylabel('No. Patients')
Text(0, 0.5, 'No. Patients')
# TODO fix legend/axis/plot alltogether
# Timeseries plt
fig, ax = plt.subplots(figsize = (16, 12))
plt.plot(arizona.date, arizona.onVentilatorCurrently, linewidth=4.7, color='r')
plt.title('Number of Patients in Arizona Currently on Ventilator', fontsize=23)
plt.xlabel('Date')
plt.ylabel('No. Patients')
Text(0, 0.5, 'No. Patients')
# Checking which cols have NaN values
arizona[['positive', 'active', 'hospitalizedCurrently', 'inIcuCurrently', 'recovered', 'death', 'hospitalized']]
arizona.head()

# Omit the NaN cols
arizona = arizona[['positive', 'active', 'hospitalizedCurrently', 'inIcuCurrently', 'recovered', 'death']]
# Scatter plots AZ
# Split dependent var from independent variables
target_az = arizona.hospitalizedCurrently
indep_var_az = arizona.drop(columns=['hospitalizedCurrently'])

fig, ax = plt.subplots(figsize = (16, 16))
for i, col in enumerate(indep_var_az.columns):
    ax=fig.add_subplot(2, 3, i+1) 
    sns.regplot(x=indep_var_az[col], y=target_az, data=indep_var_az, label=col, scatter_kws={'s':10}, line_kws={"color": "plum"})
    plt.suptitle('Distributions of Independent Variables Arizona', fontsize=23)
plt.tight_layout()
fig.subplots_adjust(top=0.95)
# Boxplot of AZ
fig, ax = plt.subplots(figsize = (16, 16))
for i, col in enumerate(arizona.columns):
    ax=fig.add_subplot(3, 3, i+1) 
    sns.boxplot(x=arizona[col], data=arizona, color='lightpink', showfliers=True)
    plt.suptitle('Boxplots of Independent Variables Arizona', fontsize=23)
plt.tight_layout()
fig.subplots_adjust(top=0.95)
###endarizona

Mississippi:

mississippi = covid_df.loc[(covid_df['abbrev'] == 'MS') & (covid_df['state']== 'Mississippi')] 
# TODO fix legend/axis/plot alltogether
# Timeseries plt
fig, ax = plt.subplots(figsize = (16, 12))
plt.plot(mississippi.date, mississippi.hospitalizedCurrently, linewidth=4.7, color='r')
plt.title('Number of Patients in Mississippi Currently Hospitalized', fontsize=23)
plt.xlabel('Date')
plt.ylabel('No. Patients')
Text(0, 0.5, 'No. Patients')
# TODO fix legend/axis/plot alltogether
# Timeseries plt
fig, ax = plt.subplots(figsize = (16, 12))
plt.plot(mississippi.date, mississippi.inIcuCurrently, linewidth=4.7, color='r')
plt.title('Number of Patients in Mississippi Currently in ICU', fontsize=23)
plt.xlabel('Date')
plt.ylabel('No. Patients')
Text(0, 0.5, 'No. Patients')
# TODO fix legend/axis/plot alltogether
# Timeseries plt
fig, ax = plt.subplots(figsize = (16, 12))
plt.plot(mississippi.date, mississippi.onVentilatorCurrently, linewidth=4.7, color='r')
plt.title('Number of Patients in Mississippi Currently on a Ventilator', fontsize=23)
plt.xlabel('Date')
plt.ylabel('No. Patients')
Text(0, 0.5, 'No. Patients')
# TODO fix legend/axis/plot alltogether
# Timeseries plt
fig, ax = plt.subplots(figsize = (16, 12))
plt.plot(mississippi.date, mississippi.death, linewidth=4.7, color='r')
plt.title('Number of Patients in Mississippi Killed', fontsize=23)
plt.xlabel('Date')
plt.ylabel('No. Patients')
Text(0, 0.5, 'No. Patients')
# Checking which cols have NaN values
mississippi[['positive', 'active', 'hospitalizedCurrently', 'inIcuCurrently', 'recovered', 'death', 'hospitalized']]
mississippi.head()

# Omit the NaN cols
mississippi = mississippi[['positive', 'active', 'hospitalizedCurrently', 'inIcuCurrently', 'recovered', 'death']]
# Scatter plots MS
# Split dependent var from independent variables
target_ms = texas.hospitalizedCurrently
indep_var_ms = texas.drop(columns=['hospitalizedCurrently'])

fig, ax = plt.subplots(figsize = (16, 16))
for i, col in enumerate(indep_var_ms.columns):
    ax=fig.add_subplot(2, 3, i+1) 
    sns.regplot(x=indep_var_ms[col], y=target_ms, data=indep_var_ms, label=col, scatter_kws={'s':10}, line_kws={"color": "plum"})
    plt.suptitle('Distributions of Independent Variables Mississippi', fontsize=18)
plt.tight_layout()
fig.subplots_adjust(top=0.95)
# Boxplot of MS
fig, ax = plt.subplots(figsize = (16, 16))
for i, col in enumerate(texas.columns):
    ax=fig.add_subplot(3, 3, i+1) 
    sns.boxplot(x=mississippi[col], data=mississippi, color='lightpink', showfliers=True)
    plt.suptitle('Boxplots of Independent Variables Mississippi', fontsize=18)
plt.tight_layout()
fig.subplots_adjust(top=0.95)
###endmississippi

Utah:

utah = covid_df.loc[(covid_df['abbrev'] == 'UT') & (covid_df['state']== 'Utah')] 
# TODO fix legend/axis/plot alltogether
# Timeseries plt
fig, ax = plt.subplots(figsize = (16, 12))
plt.plot(utah.date, utah.hospitalizedCurrently, linewidth=4.7, color='r')
plt.title('Number of Patients in UT Currently Hospitalized', fontsize=23)
plt.xlabel('Date')
plt.ylabel('No. Patients')
Text(0, 0.5, 'No. Patients')
# TODO fix legend/axis/plot alltogether
# Timeseries plt
fig, ax = plt.subplots(figsize = (16, 12))
plt.plot(utah.date, utah.inIcuCurrently, linewidth=4.7, color='r')
plt.title('Number of Patients in UT Currently in ICU', fontsize=23)
plt.xlabel('Date')
plt.ylabel('No. Patients')
Text(0, 0.5, 'No. Patients')
# TODO fix legend/axis/plot alltogether
# Timeseries plt
fig, ax = plt.subplots(figsize = (16, 12))
plt.plot(utah.date, utah.death, linewidth=4.7, color='r')
plt.title('Number of Cummulative Deaths in Utah', fontsize=23)
plt.xlabel('Date')
plt.ylabel('No. Patients')
Text(0, 0.5, 'No. Patients')
# Checking which cols have NaN values
utah[['positive', 'active', 'hospitalizedCurrently', 'inIcuCurrently', 'recovered', 'death', 'hospitalized']]
utah.head()

# Omit the NaN cols
utah = utah[['positive', 'active', 'hospitalizedCurrently', 'inIcuCurrently', 'recovered', 'death']]
# Scatter plots UT
# Split dependent var from independent variables
target_ut = utah.hospitalizedCurrently
indep_var_ut = utah.drop(columns=['hospitalizedCurrently'])

fig, ax = plt.subplots(figsize = (16, 16))
for i, col in enumerate(indep_var_tx.columns):
    ax=fig.add_subplot(2, 3, i+1) 
    sns.regplot(x=indep_var_ut[col], y=target_ut, data=indep_var_ut, label=col, scatter_kws={'s':10}, line_kws={"color": "plum"})
    plt.suptitle('Distributions of Independent Variables Utah', fontsize=23)
plt.tight_layout()
fig.subplots_adjust(top=0.95)
###endutah

Georgia:

georgia = covid_df.loc[(covid_df['abbrev'] == 'GA') & (covid_df['state']== 'Georgia')] 
# TODO fix legend/axis/plot alltogether
# Timeseries plt
fig, ax = plt.subplots(figsize = (16, 12))
plt.plot(georgia.date, georgia.hospitalizedCurrently, linewidth=4.7, color='r')
plt.title('Number of Patients in Georgia Currently Hospitalized', fontsize=23)
plt.xlabel('Date')
plt.ylabel('No. Patients')
Text(0, 0.5, 'No. Patients')
# TODO fix legend/axis/plot alltogether
# Timeseries plt
fig, ax = plt.subplots(figsize = (16, 12))
plt.plot(georgia.date, georgia.totalTestsViral, linewidth=4.7, color='r')
plt.title('Number of Cummulative Viral Tests in Georgia', fontsize=23)
plt.xlabel('Date')
plt.ylabel('No. Patients')
Text(0, 0.5, 'No. Patients')
# TODO fix legend/axis/plot alltogether
# Timeseries plt
fig, ax = plt.subplots(figsize = (16, 12))
plt.plot(georgia.date, georgia.positiveTestsViral, linewidth=4.7, color='r')
plt.title('Number of Cummulative Positive Viral Tests in Georgia', fontsize=23)
plt.xlabel('Date')
plt.ylabel('No. Patients')
Text(0, 0.5, 'No. Patients')
# TODO fix legend/axis/plot alltogether
# Timeseries plt
fig, ax = plt.subplots(figsize = (16, 12))
plt.plot(georgia.date, georgia.positiveTestsViral/georgia.totalTestsViral*100, linewidth=4.7, color='r')
plt.title('Infection Rate in Georgia', fontsize=23)
plt.xlabel('Date')
plt.ylabel('% Infection Rate')
Text(0, 0.5, '% Infection Rate')
# Checking which cols have NaN values
georgia[['positive', 'active', 'hospitalizedCurrently', 'inIcuCurrently', 'recovered', 'death', 'hospitalized']]
georgia.head()

# Omit the NaN cols
georgia = georgia[['positive', 'active', 'hospitalizedCurrently', 'inIcuCurrently', 'recovered', 'death']]
# Scatter plots GA
# Split dependent var from independent variables
target_ga = georgia.hospitalizedCurrently
indep_var_ga = georgia.drop(columns=['hospitalizedCurrently'])

fig, ax = plt.subplots(figsize = (16, 16))
for i, col in enumerate(indep_var_ga.columns):
    ax=fig.add_subplot(2, 3, i+1) 
    sns.regplot(x=indep_var_ga[col], y=target_ga, data=indep_var_ga, label=col, scatter_kws={'s':10}, line_kws={"color": "plum"})
    plt.suptitle('Distributions of Independent Variables Georgia', fontsize=23)
plt.tight_layout()
fig.subplots_adjust(top=0.95)
# Boxplot of GA
fig, ax = plt.subplots(figsize = (16, 16))
for i, col in enumerate(georgia.columns):
    ax=fig.add_subplot(3, 3, i+1) 
    sns.boxplot(x=georgia[col], data=georgia, color='lightpink', showfliers=True)
    plt.suptitle('Boxplots of Independent Variables Georgia', fontsize=23)
plt.tight_layout()
fig.subplots_adjust(top=0.95)
###endgeorgia

Alabama:

Text(0, 0.5, 'No. Patients')
# Checking which cols have NaN values
bama[['positive', 'active', 'hospitalizedCurrently', 'inIcuCurrently', 'recovered', 'death', 'hospitalized']]
bama.head()

# Omit the NaN cols
bama = bama[['positive', 'active', 'hospitalizedCurrently', 'inIcuCurrently', 'recovered', 'death']]
# Scatter plots AL
# Split dependent var from independent variables
target_al = bama.hospitalizedCurrently
indep_var_al = bama.drop(columns=['hospitalizedCurrently'])

fig, ax = plt.subplots(figsize = (16, 16))
for i, col in enumerate(indep_var_al.columns):
    ax=fig.add_subplot(2, 3, i+1) 
    sns.regplot(x=indep_var_al[col], y=target_al, data=indep_var_al, label=col, scatter_kws={'s':10}, line_kws={"color": "plum"})
    plt.suptitle('Distributions of Independent Variables Alabama', fontsize=18)
plt.tight_layout()
fig.subplots_adjust(top=0.95)
# Boxplot of AL
fig, ax = plt.subplots(figsize = (16, 16))
for i, col in enumerate(bama.columns):
    ax=fig.add_subplot(3, 3, i+1) 
    sns.boxplot(x=bama[col], data=bama, color='lightpink', showfliers=True)
    plt.suptitle('Boxplots of Independent Variables Alabama', fontsize=18)
plt.tight_layout()
fig.subplots_adjust(top=0.95)
###endalabama

Oklahoma:

oklahoma = covid_df.loc[(covid_df['abbrev'] == 'OK') & (covid_df['state']== 'Oklahoma')] 
# TODO fix legend/axis/plot alltogether
# Timeseries plt
fig, ax = plt.subplots(figsize = (16, 12))
plt.plot(oklahoma.date, oklahoma.hospitalizedCurrently)
plt.title('Number of Patients in OK Currently Hospitalized')
plt.xlabel('Date')
plt.ylabel('No. Patients')
Text(0, 0.5, 'No. Patients')
# Checking which cols have NaN values
oklahoma[['positive', 'active', 'hospitalizedCurrently', 'inIcuCurrently', 'recovered', 'death', 'hospitalized']]
oklahoma.head()

# Omit the NaN cols
oklahoma = oklahoma[['positive', 'active', 'hospitalizedCurrently', 'inIcuCurrently', 'recovered', 'death']]
# Scatter plots OK
# Split dependent var from independent variables
target_ok = oklahoma.hospitalizedCurrently
indep_var_ok = oklahoma.drop(columns=['hospitalizedCurrently'])

fig, ax = plt.subplots(figsize = (16, 16))
for i, col in enumerate(indep_var_ok.columns):
    ax=fig.add_subplot(2, 3, i+1) 
    sns.regplot(x=indep_var_ok[col], y=target_ok, data=indep_var_ok, label=col, scatter_kws={'s':10}, line_kws={"color": "plum"})
    plt.suptitle('Distributions of Independent Variables OK', fontsize=18)
plt.tight_layout()
fig.subplots_adjust(top=0.95)
# Boxplot of OK
fig, ax = plt.subplots(figsize = (16, 16))
for i, col in enumerate(oklahoma.columns):
    ax=fig.add_subplot(3, 3, i+1) 
    sns.boxplot(x=oklahoma[col], data=oklahoma, color='lightpink', showfliers=True)
    plt.suptitle('Boxplots of Independent Variables OK', fontsize=18)
plt.tight_layout()
fig.subplots_adjust(top=0.95)
###endoklahoma

Assessing Correlation of Independent Variables

# TODO add some explanation / look more into collinear variables
# Heatmap of correlations
# Save correlations to variable
corr = covid_cleaned.corr(method='pearson')
# We can create a mask to not show duplicate values
mask = np.triu(np.ones_like(corr, dtype=np.bool))
# Set up the matplotlib figure
fig, ax = plt.subplots(figsize=(16,16))

# Generate heatmap
sns.heatmap(corr, annot=True, mask=mask, cmap='GnBu', center=0,
            square=True, linewidths=.5, cbar_kws={"shrink": .5})
<matplotlib.axes._subplots.AxesSubplot at 0x2659c25b148>

Build model for dependent Variable

  • To be used to predict current hospitalizations
  • Having more complete variables for in ICU currently and on Ventilator Currently will allow us to predict these numbers as well.
# We compare three models:
# - Polynomial Regression
# - Linear Regression
# - ElasticNet

# Copy DFs to not mess up original one
# We will use model_df for our regression model
model_df = all_cases.copy()

# Delete redundant rows
for row in ['abbrev', 'bedsPerThousand', 'hospitalized', 
'state', 'hospitalizedCumulative', 'dataQualityGrade', 'lastUpdateEt']:
    del model_df[row]

# Drop NaN values for hospitalizedCurrently
model_df = model_df.dropna(subset=['hospitalizedCurrently'])

# Drop Values with abnormal active-hospitalised ratios (outside Conf. Interval)
model_df['ratio_hospital'] = model_df['hospitalizedCurrently'] / model_df['active']
model_df = model_df[~(model_df['ratio_hospital'] >= model_df.ratio_hospital.quantile(0.99))]

#model_df = model_df[~(model_df['ratio_hospital'] <= model_df['ratio_hospital'].median())]
del model_df['ratio_hospital']

# Get peek of model to use
model_df.describe()
population positive active hospitalizedCurrently inIcuCurrently onVentilatorCurrently recovered death totalTestsViral positiveTestsViral negativeTestsViral positiveCasesViral commercialScore negativeRegularScore negativeScore positiveScore score grade total_beds
count 4016.000 4016.000 4016.000 4016.000 2003.000 1742.000 4016.000 4016.000 1330.000 443.000 448.000 2865.000 4016.000 4016.000 4016.000 4016.000 4016.000 0.000 4016.000
mean 6599959.463 32056.054 29434.070 972.991 431.098 213.277 7421.054 1691.020 407045.865 26715.413 266489.357 36657.264 0.000 0.000 0.000 0.000 0.000 nan 15743.010
std 7580186.713 56930.101 52375.376 1867.806 705.513 322.794 13907.212 3589.041 578843.972 26788.099 249374.376 61167.272 0.000 0.000 0.000 0.000 0.000 nan 16214.148
min 567025.000 115.000 113.000 1.000 1.000 0.000 0.000 0.000 2857.000 407.000 8648.000 396.000 0.000 0.000 0.000 0.000 0.000 nan 1318.928
25% 1778070.000 3253.000 3050.000 103.000 75.000 32.000 35.000 89.000 72778.750 4150.000 66084.000 6277.000 0.000 0.000 0.000 0.000 0.000 nan 3773.952
50% 4499692.000 12498.500 11350.000 390.500 165.000 90.000 1513.000 459.000 213753.000 14498.000 186700.000 16661.000 0.000 0.000 0.000 0.000 0.000 nan 11557.920
75% 7797095.000 35407.250 32086.000 927.500 430.000 228.000 6783.750 1506.500 499122.500 46468.000 380138.250 40786.000 0.000 0.000 0.000 0.000 0.000 nan 19124.737
max 39937489.000 395872.000 553611.000 18825.000 5225.000 2425.000 93572.000 24885.000 4448176.000 89648.000 1036090.000 395872.000 0.000 0.000 0.000 0.000 0.000 nan 71887.480
### Mark Bee (https://www.facebook.com/markbeenyc) - do you need a sippy cup lesson on this information?